Picture-based insurance: is it sustainable?

Effects on Willingness to Pay, Adverse Selection, and Moral Hazard

Picture-Based Crop Insurance (PBI) offers a new way of delivering affordable and easy-to-understand crop insurance, using farmers’ smartphone pictures to minimize the costs of loss verification. Millions of smallholder farmers lack access to affordable insurance because their farms are simply too small and too remote for insurers to affordably verify damage on insured crops. However, with improvements in technology, insurance companies may no longer need to send an insurance agent to verify a farmer’s claim in person. They could simply appraise losses by processing smartphone pictures of damaged crops, taken by farmers themselves, as long as these pictures reliably document crop damage due to a natural disaster and document that crops were managed appropriately until that event.

A previous project note (Kramer et al., 2017) [1] shows that farmers are able and willing to take such pictures, that crop damage is visible and quantifiable through pictures, and that picture-based loss assessment can capture damage that a weather index-based insurance product would not be able to detect. Thus, from a practical point of view, PBI seems to be a viable insurance approach that is worth developing further for implementation at a larger scale. However, insurance markets may fail not only because of high loss verification costs, but also because of low demand, adverse selection (farmers enrolling plots more prone to damage), and moral hazard (farmers reducing effort on their plots once insured). These factors could raise insurance premiums to unsustainable levels, crowding out demand.

This project note hence describes to what extent picture-based crop insurance is viable from an economic point of view, addressing the following questions: (1) Do farmers strategically reduce crop management efforts (that is, does PBI induce moral hazard) and is there evidence of tampering with pictures in order to receive payouts when they have PBI coverage? (2) What is farmers’ willingness to pay for PBI compared to willingness to pay for standard weather index-based insurance (WBI)? Is farmers’ demand for PBI strong enough to justify its higher costs? (3) To what extent do farmers selectively enroll plots that are more prone to damage? In other words, is PBI prone to adverse selection? We will answer these questions using the results from a formative evaluation of PBI in six districts of Haryana and Punjab, India.

Methods and data

We describe one study focusing on moral hazard, and one — using a sub-sample of farmers — focusing on demand and adverse selection.

Testing for moral hazard

A randomized trial with farmers from 50 villages in Haryana and Punjab, India randomly assigned villages to one of two treatment arms:

Within every village, 15 farmers completed a baseline survey in July 2016. These farmers were randomly selected from a list of all farmers within the village who satisfied the following criteria: (1) having less than 15 acres of operational farmland and (2) planning to grow at least two acres of wheat during the upcoming Rabi (winter) season. Of these invited farmers, 592 (approximately 12 farmers per village) agreed to regularly take pictures, using a smartphone app, of one acre of their wheat crop during the Rabi 2016-2017 season, in return for a monthly data plan and insurance coverage. Depending on the random assignment of treatment, these famers received either the WBI + pictures or WBI + PBI product for the one acre of wheat of which they were taking pictures.

Procedures for taking pictures were designed to be scalable, but in such a way that they would minimize moral hazard or potential tampering with pictures. To enroll, farmers took an initial overview picture of their plot, facing north. Farmers were asked to take three repeat pictures per week throughout the entire season, taken from the exact same location and with the same view angle as the initial picture. To facilitate this, the smartphone app included geo-tags and visual aids in the form of a “ghost” image (a partially transparent image of the initial picture) that allowed the farmer to align static features in the landscape (such as distant trees or structures) with those in the initial picture. Valid pictures were then uploaded to an online server and processed by the research team. Importantly, loss assessment relied on the time series of overview pictures instead of on snapshots zoomed in on damaged crops because the latter approach would be more susceptible to tampering.

One objective of the project is to develop automated image processing algorithms for loss assessment based on crop pictures. Since such algorithms were not yet available, at the end of the study season, an independent panel of wheat experts inspected the time series of pictures for visible damage due and not due to mismanagement of the crop. Experts assessed whether there had been any damage to the crop and, if damage had occurred, the percentage by which the crop had been damaged. Although these loss assessments were used to determine insurance payouts only for farmers with WBI + PBI coverage, experts also reviewed pictures from farmers with the WBI + pictures product (without knowing the type of coverage to which the farmer had been assigned). In the presence of moral hazard, we would expect observed crop damage to be more severe for farmers with PBI coverage (that is, for farmers with the WBI + PBI product).

In addition to the damage estimates, we rely on objectively measured yields and self-reported input use to test for moral hazard. Wheat yields were measured through crop cutting exercisesjust prior to harvest. In the presence of moral hazard, we would expect yields to be lower among farmers with the WBI + PBI product. Moreover, we look for evidence of reduced effort by farmers with PBI coverage by testing for lower usage of fertilizers, pesticides (including herbicides and fungicides), and farm labor. Farmers reported these input variables for the photographed plot during an endline survey just after harvest in May-June 2017.

Demand and adverse selection

During July 2017, several months before the start of the Rabi wheat production season (November-December), we conducted an additional study to understand other important aspects of product sustainability: the strength of demand for PBI and the degree of adverse selection into the product. To address these questions, the study elicited willingness to pay from a sub-sample of 100 farmers for four different products:

WBI only: offering coverage against excess rainfall and abovenormal temperatures, without having to take crop pictures.

WBI + pictures: the WBI product, but paying out only if the farmer regularly takes pictures of the insured plot.

WBI + PBI: the same product as WBI + pictures, but providing additional coverage against damage visible in the pictures.

PBI only: covering only against damage visible in the pictures

This design allows us to analyze several aspects of the demand for these products. One of our primary outcomes is the difference in willingness to pay between WBI only and WBI + PBI, which indicates how much farmers are willing to pay for extra PBI coverage. Other comparisons provide further insights regarding farmers’ perception of these products. The comparison of WBI + pictures and WBI only reveals farmers’ utility (or disutility) derived from having to take pictures regularly, providing an objective valuation of this implicit condition for receiving payouts from the PBI component. Comparing WBI + pictures and WBI + PBI quantifies farmers’ valuation of picture-based loss assessment conditional on taking pictures regularly; comparing WBI + PBI and PBI only indicates how much farmers value WBI coverage and whether demand for WBI + PBI could be enhanced by reducing or removing WBI coverage.

Farmers described their willingness to pay for a product that would cover one acre of Rabi wheat, and they had the freedom to choose which one plot to insure. By providing the farmer with this choice, we can compare the characteristics of plots that the farmer chose to enroll and plots that the farmer did not choose to enroll. These analyses provide information on the degree of adverse selection; that is, whether farmers selectively enrolled plots with an increased risk of damage, and hence increased chance for insurance pay-outs – for instance, plots poor access to irrigation or with poor soil quality. We will also test whether the amount that farmers are willing to pay extra for WBI + PBI compared with WBI only is higher for farmers whose crops are more likely to incur damage.

Willingness to pay was elicited using the Becker-Degroot-Marschak (BDM) method. Specifically, each farmer received a scratch card (see Figure 1 for an example) with illustrations of each product and – hidden under metallic scratch-off ink – a randomly assigned premium offer for a randomly selected product (e.g. Rs. 1,800 for WBI + PBI, in Figure 1). Farmers were instructed to write their maximum willingness to pay for each of the four products in the top panel before scratching off the ink to reveal the special offer. If a farmer’s willingness to pay for the selected product was at or above the pre- 3 mium offer, he would purchase the product at that premium. Otherwise, the farmer would not be able to purchase any of the products at that time. This gave farmers incentives to reveal their maximum willingness to pay, as writing down a lower amount could result in them losing out on a lower special premium and as stating a higher amount could result in them having to purchase a product at a premium that they were unwilling to pay.

Figure 1 – Scratch card to elicit willingness to pay

Results

1. Do farmers strategically put less effort into crop management when covered by PBI? Is there moral hazard?

The first question pertains to whether PBI induces moral hazard on insured plots. To that end, Figure 2 compares input usage for the Rabi 2016-2017 season (self-reported during the endline survey) for two types of farmers: those who received the WBI + pictures product and those who received the WBI + PBI product. If being covered against damage visible in pictures motivated farmers to put less effort into crop management, we would expect lower average input usage from farmers in the WBI+PBI group. However, Figure 2 shows that the usage of fertilizers (left chart), pesticides, fungicides, and herbicides (middle chart), and farm labor (right chart) is statistically indistinguishable between the two types of farmers. Thus, at the most direct level of input usage, we find no evidence of moral hazard.

Figure 2 – PBI coverage does not affect the quantity of inputs used

Figure 3 – PBI coverage does not affect yields or assessed damage

Interestingly, during focus group discussions, farmers reported that having to take pictures actually improved their management practices. Since they visited their field more often, they were able to monitor their crops more closely and detect weeds, pests, or or diseases at an earlier stage. Since both farmers with WBI + PBI coverage and those with only WBI coverage were required to take pictures, we cannot test this mechanism more formally, but this could be an area for future research.

It is possible that PBI induces moral hazard in ways not captured by input usage. Hence, we also test for moral hazard in a second set of outcome variables, which are objectively measured instead of self-reported. Figure 3 compares wheat yields (measured through crop cutting exercises) and damage due and not due to mismanagement (assessed by agronomic experts) for farmers with the WBI + pictures product and farmers with WBI + PBI product. Moral hazard would result in lower yields and higher damage for farmers with PBI coverage than for farmers taking pictures but not receiving PBI coverage. Nonetheless, we find no significant effect of PBI coverage on either yields or assessed damage, providing further evidence that farmers did not strategically worsen their crop management to receive insurance payouts

In sum, regardless of whether we analyze self-reported input usage, objectively measured yields, or damage assessed by agronomic experts, we find no evidence that farmers with PBI coverage reduce their efforts in response to having coverage against damage visible in pictures. Thus, at least in this specific context and during this first season, one of the main weaknesses often attributed to indemnitybased insurance — moral hazard — did not play a role.

2. Do farmers prefer PBI coverage over WBI coverage?

If so, by how much? The second question pertains to whether farmers’ valuation of PBI is sufficiently high to justify its additional costs. Figure 4 presents the average willingness to pay for each of the four products – that is, the average amount that farmers wrote down as the maximum amount they would pay for the four products on the scratch card.

Figure 4 shows that farmers are willing to pay Rs. 736 for the WBI only product (left bar). Interestingly, the willingness to pay for the WBI + pictures product — which requires farmers to take pictures of their crops regularly throughout the Rabi season — was only Rs. 21 lower, which is not a statistically significant difference. Contrary to our expectations, having to take pictures of their crops on a regular basis did not reduce farmers’ willingness to pay by a significant amount. On the other hand, farmers were willing to pay Rs. 866 on average for the PBI only product, which is a significant Rs. 129 higher than their willingness to pay for the WBI only product. Finally, the WBI + PBI product, which bundles WBI and PBI coverage, increased willingness to pay by a significant Rs. 338 (to Rs. 1,052).

It is important to compare farmers’ willingness to pay for these different products with the premium at which the product would be offered under real market conditions. The average willingness to pay for WBI only is only 21.2 percent of the actual insurance premium of Rs. 3,473. In addition, the insurance premium for the WBI + PBI product was Rs. 660 higher than willingness to pay, at Rs. 4,133. Thus, the average farmer also does not seem willing to pay the full cost of the additional PBI coverage. It is important to note, however, that without historical data to price the PBI product, this Rs. 660 included an additional uncertainty premium. In addition, wheat yields are relatively stable in Haryana and Punjab, which could further reduce the premium mark-up. Thus, in our study area, Rs. 660 is most likely an upper bound of the amount by which the insurance premium should increase when adding PBI coverage to a WBI product.

An alternative way of presenting farmers’ valuation of PBI is by summarizing the payout that a farmer expects to receive on average per year from a given insurance product. After eliciting their willingness to pay, we asked farmers to indicate the probability of incurring damage due to extreme heat, excess rainfall, or lodging; the probability of the product to pay out in such an event; and the expected payout conditional on receiving a payout. We also asked them to indicate the probability of the product paying out if there was no damage and how much the product would pay in that case. This allows us to construct an expected average payout, summarized for the median farmer by the green bars in Figure 5. For WBI only, the median farmer expected to receive Rs. 720; for PBI only, this is Rs. 1040, while for WBI + PBI, the expected payout is Rs. 1800.

The findings are interesting and perhaps somewhat surprising. Farmers on average expect to receive much higher payouts, especially for picture-based coverage added to a WBI product, than what they are willing to pay. This suggests that the median farmer would not be willing to take insurance at premiums that would be required to meet that farmer’s expectations. At the same time, this premium calculated based on farmers’ expectations could be interpreted as the extent to which farmers value the product. Given that farmers expect much higher payouts on average under WBI + PBI compared to WBI only and PBI only, this could mean that farmers really value the bundled product but that other constraints prevent them from paying the full premium required to receive such high payouts on average per year.

What do these findings mean? First, farmers’ demand for PBI is stronger than for WBI, and having to take pictures does not appear to be a major barrier to enroll. Second, farmers also expect to receive higher payouts and more complete insurance from a product that includes PBI. However, their willingness to pay remains too low to market the product under the current conditions. Part of this might be related to the timing of the willingness to pay elicitation; farmers were surveyed in July, four months before land preparation for the wheat crop. Thus, risk in wheat production may not have been very salient at this time, and farmers may have preferred to wait until after the Kharif harvest before deciding to purchase insurance for the next season.

Nevertheless, in the absence of premium subsidies for PBI, it would be important to further improve farmers’ willingness to pay. One potential way of doing so could be through insurance education, specifically by discussing with farmers their expectations of the product’s payouts and how much the premium associated with that average payout would need to be for the product to be sustainable. This seems especially important for the bundled product, for which farmers expect double the payouts but are not willing to pay double the premium.

Another potential channel for improvement is to design premium collection in such a way that liquidity constraints do not depress demand. We find that wealthier farmers and farmers who took out a loan in the previous season (which reflects, among other things, better access to credit) are willing to pay nearly Rs. 300 more for insurance than farmers who had not taken out a loan. This suggests that liquidity constraints reduce willingness to pay. In future work, it will be important to address such liquidity constraints, for instance by deferring premium payments until the end of the season.

3. Do farmers selectively enroll plots into PBI? Is there adverse selection?

The final question addressed in this project note relates to adverse selection, or the tendency for insurance to be purchased by farmers who are most at risk (who value it the most, since they expect the highest payouts) and for their most vulnerable plots, at the expense of farmers and plots at risk of the least damage (and hence the lowest insurance payouts). This tendency would induce insurance companies to raise premiums and further crowd out farmers and plots at lower risk, creating a feedback loop that could make the product unsustainable.

We study two aspects of adverse selection: at the farmer level and at the plot level. For the former, we test whether farmers with worse yields and farmers for whom experts detected damage in the crop pictures (arguably the riskier farmers) are willing to pay relatively more for PBI insurance coverage. For the latter aspect, given that farmers had to select one plot to be insured, we test whether they tend to select plots with worse (i.e. riskier) characteristics.

First, we test whether there is a correlation between–on one hand—a farmer’s yields and the degree of damage visible in the farmer’s pictures and—on the other hand—the amount that a farmer is willing to pay extra to purchase WBI + PBI instead of WBI only. In the presence of adverse selection, this extra willingness to pay would be higher for farmers with a higher chance of insurance payouts, that is, farmers with lower yields or more severe damage. However, the willingness to pay for extra coverage does not decrease based on a farmer’s yields and is also not significantly higher for farmers with visible damage in their pictures (Figure 6). Thus, we find no evidence of adverse selection in the willingness to pay for extra PBI coverage.

Figure 6 – Willingness to pay for extra PBI coverage is not predicted by past yields or assessed damage

As a next step, we test whether the plots that farmers choose to enroll in insurance are of lower quality, or of higher risk, than the plots that they choose not to enroll in insurance. To that end, Figure 7 shows comparisons of various quality indicators between the plot that the farmer selected to enroll in insurance and the other plots that the farmer opted not to enroll. Across a number of important characteristics related to a plot’s riskiness – such as how far the plot is from an irrigation source, previous year’s yields, the plot’s sales and rental value per acre, whether the plot has good drainage, and whether the plot has good soil fertility – we find no quality differences between those plots selected and those not selected for PBI coverage. Thus, at the plot level, we also find no evidence of adverse selection.

Figure 7 – The plot that the farmer chooses to enroll has the same quality as his other plots

Conclusions

In conclusion, PBI is a promising approach to reduce the costs of loss verification and to improve product ownership among smallholder farmers, by relying on pictures from inexpensive smartphone cameras taken by farmers themselves. Farmer expectations about PBI payouts, especially during bad years, indicate that they believe this product is able capture damage considerably better than weatherindex based alternatives. As a result, they are willing to pay more for PBI than for more traditional WBI products. In addition, farmers are willing to pay more for PBI coverage than the hypothetical premiums calculated from the average payouts made in Rabi 2016-2017 season, suggesting that the product could be offered sustainably in real market conditions if true premiums were adjusted for actual losses (plus a reasonable loading factor). Moreover, the need for farmers take pictures on a regular basis was not a major factor determining their willingness to pay, suggesting that this approach is indeed feasible.

At the same time, while willingness to pay for the bundled product that combines WBI and PBI coverage is considerably higher than that for the WBI coverage alone, it remains far below the hypothetical insurance premium implicit in farmers’ expectations about payouts. On the one hand, this means that farmers see the value of a picture-based insurance approach. On the other hand, they are either not willing or not able to pay the extra costs associated with such insurance. Solutions that help relax liquidity constraints (for instance, deferred premium payments or bundling with loans) and insurance education may help better align farmers’ willingness to pay with insurance premiums.

Multi-peril indemnity insurance products that cover farmers against actual damage, as opposed to pre-specified weather conditions, are often considered to be subject to moral hazard and adverse selection; however, we find no evidence that the PBI approach induces either. This could indeed be a positive consequence of farmers regularly taking pictures, which may give them the impression that the insurance company is watching them and that they cannot reduce efforts or tamper with the pictures to trigger insurance payouts without being noticed. One caveat worth mentioning in this regard is that as farmers become more acquainted with the product, moral hazard and adverse selection may arise over time. Testing for such mechanisms remains important in the monitoring and evaluation of PBI approaches.

Take-away messages

Farmers are willing to pay more for picture-based insurance (PBI) than for weather index-based insurance (WBI).

[2] Since this measure captures variability in only one season, the actual — but unknown — expected payout may deviate from this, especially for weather, which is a covariate risk that varies more over time than over space (at least in our study, which included only six districts from two states). It would be expected, though, that especially for the PBI add-on, the average payout plus 30 percent loading could be a reasonable proxy, because the added value of PBI is mostly in observing localized risks such as hail and lodging that are more idiosyncratic in nature.

Disclaimer

The following article has been previously published. The GBG Fund makes no proprietorial claim to content and claims no editorial responsibility. The article appears here with the kind permission of the author and the original publisher.